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modeling of a hydroelectric unit with a Kaplan turbine 4

Modeling of a hydroelectric unit with a Kaplan turbine

引用

4th international conference on control in Technical systems, CTS 2021

作者： Ivanova, Ksenia A. Karuna, Evgeniy N. Sokolov, Petr V. Automatic Control Systems Department Saint Petersburg Electrotechnical University "LETI" St. Petersburg Russia

ISBN: (纸本)9781665424547

The publication is devoted to the development of a model and research of the hydroelectric unit of a power plant with a Kaplan turbine. Matlab/Simulink environment was used for modeling. The main problems, features and approaches to the modeling of hydraulic units are discussed. The specificity of the proposed model is to take into account the nonlinear characteristics of the system and the influence of a synchronous generator operating on a bus of infinite power. It makes it possible to study the operation of the system in a wide range of modes. Equations and block diagrams of the main subsystems of the hydroelectric unit are given. The issues of adjusting the controllers of the hydraulic unit are considered. The graphs with the research results are presented and analyzed. Conclusions about the effectiveness of the obtained model are made and further ways of research are outlined. © 2021 IEEE.

关键词： Synchronous generators

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Reinforcement Learning Algorithms Performance Evaluation for Management problems Decision in Socio-Economic systems

Reinforcement Learning Algorithms Performance Evaluation for...

引用

international conference on control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ekaterina V. Orlova Department of Economics Management and Business Ufa University of Science and Technology Ufa Russia

The problem of reinforcement learning algorithms selection which adequately describe the stochastic dynamics of the modeled object and have high performance is very important. Business and quality metrics that are appropriate for assessing the quality of supervised and unsupervised learning methods in machine learning are not entirely suitable for evaluating the effectiveness of reinforcement learning methods, since there is no empirical data for evaluation. The paper proposes the quality indicators for generated on the basis of reinforcement lerning methods managerial decisions. We use an example for the corporate human capital management. A comparison of learning algorithms sush as DQN, DDQN, SARSA, PRO for designing optimal trajectories for the professional employees development is made. We compare reinforcement learning algorithms used proposed quality indicators and choose one with the highest performance.

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Synthesis of UAV Motion control Algorithms along a Given Trajectory

Synthesis of UAV Motion Control Algorithms along a Given Tra...

引用

2022 international Russian Automation conference, RusAutoCon 2022

作者： Kreerenko, O.D. Moscow Aviation Institute Moscow Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

Unmanned aerial vehicles (UAVs) are increasingly entering various spheres of human life. They are used in agriculture, to protect forests from fires, to ensure road safety, to monitor the condition of oil and gas pipelines, and so on. The scope of the UAV is constantly expanding. The range of tasks they solve becomes more complicated. This leads to the complication of the UAV as a control object (CO). Therefore, the control systems of such objects are also facing more and more serious problems, the requirements for accuracy and speed of their functioning are becoming more stringent. Thus, for the synthesis of UAV control algorithms, new approaches are required that will allow solving the problem in a fairly general form based on the mathematical model of the object, taking into account the natural physical properties of controlled processes. Such an approach is the synergetic approach in control theory. In this paper, we consider the procedure for the synergistic synthesis of UAV control algorithms when moving along a predetermined trajectory at a given speed. When modeling transient processes, the dynamic properties of the resulting closed system 'UAV - autopilot' were investigated. It is shown that the synthesized control algorithms ensure the asymptotic stability of a closed nonlinear system in the entire admissible range of phase coordinates. The exact fulfillment of the goals of management has been demonstrated. In addition, the influence of external disturbances on the functioning of the closed system 'UAV-autopilot' is considered. © 2022 IEEE.

关键词： Asymptotic stability

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7th international Symposium on Digital Instrumentation and control Technology for Nuclear Power Plant, SICPNPP 2023

7th International Symposium on Digital Instrumentation and C...

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7th international Symposium on Digital Instrumentation and control Technology for Nuclear Power Plant, SICPNPP 2023

ISBN: (纸本)9789819934546

The proceedings contain 43 papers. The special focus in this conference is on Digital Instrumentation and control Technology for Nuclear Power Plant. The topics include: An Industrial control System Vulnerability Analysis Method for Cyber Security in Nuclear Power Plant;study of Optimization for Calibration of Nuclear Safety Classification Pressure Transmitter;modeling and Simulation of Secondary Loop systems of a Pressurized Water Reactor Based on the 3KEYMASTER Platform;Treatment and Analysis of SG Level control problems in Nuclear Power Plant Startup and Low Power Stage;Weakness Analysis and Improvement on the Technical Galleries and Gutters Fire in CPR1000 Nuclear Power Plant;Response Time Analysis Method of Safety-Level DCS in Nuclear Power Plant Based on Probability Theory;rational Argument of the Closed Loop of the Reactor Spent Fuel;pressurizer control Optimization with Deep Learning-Based Predictions;study on High Flux at Shutdown Alarm Setpoint Automatic Update Logic;research and Application of Endogenous Security Active Defense Technology for Domestic Nuclear Safety-Level Gateway Equipment;i&C Design for Heat Supply Retrofit in Nuclear Power Plant;research and Application of Current Following Electrical Fire Monitoring Technology;Prediction of Electromagnetic Shielding Effectiveness of DCS Cabinet Based on Neural Network;Design and Implementation of DCS Design List Automatic Generation and Check System Based on LabVIEW;Reliability Analysis of Nuclear Safety-Class DCS ESFAS Based on FTA;research on Forward Design Process of Network Security Protection Design for Instrument and control System in Nuclear Power Plant;application of Optocoupler Isolation in Rotation Speed Measuring Circuit of Maglev Blower.

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NSP: A Neuro-Symbolic Natural Language Navigational Planner 23

NSP: A Neuro-Symbolic Natural Language Navigational Planner

引用

23rd IEEE international conference on Machine Learning and Applications, ICMLA 2024

作者： English, William Simon, Dominic Jha, Sumit Kumar Ewetz, Rickard University of Florida GainesvilleFL32611 United States Florida International University Computer Science Department MiamiFL33199 United States

ISBN: (纸本)9798350374889

Path planners that can interpret free-form natural language instructions hold promise to automate a wide range of robotics applications. These planners simplify user interactions and enable intuitive control over complex semi-autonomous systems. While existing symbolic approaches offer guarantees on the correctness and efficiency, they struggle to parse free-form natural language inputs. Conversely, neural approaches based on pre-trained Large Language Models (LLMs) can manage natural language inputs but lack performance guaran-tees. In this paper, we propose a neuro-symbolic framework for path planning from natural language inputs called NSP. The framework leverages the neural reasoning abilities of LLMs to i) craft symbolic representations of the environment and ii) a symbolic path planning algorithm. Next, a solution to the path planning problem is obtained by executing the algorithm on the environment representation. The framework uses a feedback loop from the symbolic execution environment to the neural generation process to self-correct syntax errors and satisfy execution time constraints. We evaluate our neuro-symbolic approach using a benchmark suite with 1500 path-planning problems. The experimental evaluation shows that our neuro-symbolic approach produces 90.1% valid paths that are on average 19-77% shorter than state-of-the-art neural approaches. © 2024 IEEE.

关键词： Motion planning

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Robotic control Optimization Through Kernel Selection in Safe Bayesian Optimization

Robotic Control Optimization Through Kernel Selection in Saf...

引用

IEEE international conference on Robotics and Biomimetics

作者： Lihao Zheng Hongxuan Wang Xiaocong Li Jun Ma Prahlad Vadakkepat School of Data Science The Chinese University of Hong Kong Research Intern at the Singapore Institute of Manufacturing Technology A*STAR Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research (A*STAR) Singapore Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open problem. In this paper, we propose a novel learning-based control optimization method, which enhances the additive Gaussian process-based Safe Bayesian Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a representative example and test the method on Safe control Gym, a benchmark designed for evaluating safe control techniques. We show that the proposed method provides a more efficient and optimal solution for high-dimensional control optimization problems, demonstrating significant improvements over existing techniques.

关键词： Additives Trajectory tracking Optimization methods control systems Bayes methods Kernel Robots Tuning Standards Quadrotors

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Multiple–Model UKF/CKF State Estimation for Nonlinear systems

Multiple–Model UKF/CKF State Estimation for Nonlinear Syste...

引用

international conference on Guidance, Navigation and control, ICGNC 2020

作者： Shi, Xiaodi Yan, Liping Xia, Yuanqing Xiao, Bo Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811581540

In most control systems, modeling error and noise interference will always lead to the performance degradation and divergence of the UKF or the CKF. To settle a matter caused by model uncertainties, a new UKF/CKF frame combined with multiple model method is presented in this paper. Through probabilistic multiple model design method, this paper approximates the posterior densities by a finite number of probabilistically weighted points and uses these points to display the entire state space. Simulation results and comparison analysis demonstrate that the multiple-model UKF(MMUKF) and the multiple-model CKF(MMCKF) have higher precision and stronger robustness than the traditional UKF and CKF in case of model uncertainties. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Uncertainty analysis

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Fuzzy Action-Masked Reinforcement Learning Behavior Planning for Highly Automated Driving 8

Fuzzy Action-Masked Reinforcement Learning Behavior Planning...

引用

8th international conference on control, Automation and Robotics, ICCAR 2022

作者： Rudolf, Thomas Gao, Mingxiao Schurmann, Tobias Schwab, Stefan Hohmann, Soren Fzi Research Center For Information Technology Karlsruhe76131 Germany Institute Of Control Systems Karlsruhe Institute Of Technology Karlsruhe76131 Germany

ISBN: (纸本)9781665481168

Highly-automated driving relies on informed decision making and safe control in a dynamic environment which is regulated by traffic rules. This complex environment can be formulated as a map-based problem where the driving task is constrained by kinematics, obstacles, traffic, and road structure. Though, solving the problem with a monolithic approach additionally can lead to infeasible conditions which are difficult to bypass. Therefore, we expect a separate layer for sequential decision making to be a more suitable approach to derive a lateral and longitudinal behavior. In this area of decision making, reinforcement learning (RL) is a viable approach to complex and nonlinear problems. However, the learning convergence to a reasonable behavior in a critical and constrained environment can be slow. To overcome this challenge, we propose an action-masked RL agent utilizing fuzzy traffic rule descriptions. The agent outputs are hybrid lateral and longitudinal actions based on the environment observations. These driving decisions interface the subsequent trajectory planner and control for optimal execution. In exemplary merging scenarios, we show the effectiveness of the masked agent to increase convergence speed toward a reasonable behavior. © 2022 IEEE.

关键词： Reinforcement learning

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Feed Drive control System with Compensation for Friction, Backlash, and Resonance Effects

Feed Drive Control System with Compensation for Friction, Ba...

引用

Informatics, Electronics, and Radio Engineering (PIERE), international conference problems of

作者： Alexander A. Alekseev Vladimir V. Tyutikov Department of Electromechanical Engineering Ivanovo State Power Engineering University Ivanovo Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

The article deals with development of a control system for the feed drive of metal-cutting machines and machining centers operating under computer numerical control which enhances machining quality by compensating for the mechanical factors inherent in the axis drive. A mathematical description of the mechanical part of the feed drive is provided, taking into account the effects of resonance, backlash, and friction. A control system has been developed, which includes functional blocks aimed at reducing these effects impact —an anti-resonance filter, a dual-loop feedback control, and segmental friction compensation. The industry-standard circle tests were used for the verification of the resulting contouring quality. The results of mathematical modeling in the Matlab Simulink suite, as well as experiments on the actual industrial lathe, demonstrate that the application of the control system with the proposed blocks reduces the deviation of the actual trajectory from the specified one by a factor of three.

关键词： Software packages Friction Machining Drives control systems Resonance Mathematical models Trajectory Feeds MATLAB

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LSTM-Based Learning control for Biomimetic Robotic Fish Swimming 14

LSTM-Based Learning Control for Biomimetic Robotic Fish Swim...

引用

14th international conference on Signal Processing systems, ICSPS 2022

作者： Wang, Ming Wang, Kunlun Zhang, Yanlu Yu, Junzhi School of Information and Electrical Engineering Shandong Jianzhu University Shandong Jinan250101 China College of Engineering Peking University Department of Mechanics and Engineering Science Beijing100871 China

ISBN: (纸本)9798350336313

To precisely manage the location and trajectory of a biomimetic robotic fish moving in water, it is crucial to comprehend its pressure and derive the related dynamic equation. In the process of dynamic modeling, the movement of robotic fish is affected by its shape, mechanical structure, and complex fluid force, making it difficult to precisely regulate the robotic fish's movement. Consequently, this article employs an LSTM (Long Short-Term Memory) neural network to discover the variations in robotic fish joint angles. Meanwhile, simulations and experiments of LSTM-controlled bionic robot fish swimming straight and turning are presented. These simulated and experimental results validate the method's efficiency. © 2022 IEEE.

关键词： Long short-term memory

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